Multimedia information is often stored on the Internet. Conventional search engines are limited in their ability to access this information. Content based information retrieval systems have been used for automatically indexing and accessing this kind of information. These systems access and index large amounts of information. Multiple features including color, texture, shape and the like are extracted from the query signals. Retrieval is then performed using a similarity matching, where the different features are matched against similar patterns. Given an input feature pattern, the matching attempts to search for similar patterns within the database.
Content based image retrieval systems leave a semantic gap between the low level features that they index, and the higher-level human concepts. Many different attempts have been made to design techniques that introduce the user into the searching loop, to enable the system to learn a user's particular preferences of query.
Relevance feedback can be used to allow the user to interactively tune the system to their own interest. This kind of feedback can be used to assess whether certain proposed images are relevant to their query or not relevant. The system learns from the examples using a machine learning technique, which is used to tune the parameters of the search. It returns a new set of similar images, and iteratively repeats the process until the user is satisfied with the result. The action is a query updating scheme, and hence can be regarded as a machine learning task.
Techniques of relevance feedback in content based information retrieval systems have conventionally used feature re-weighting. The weights associated with each feature for a K nearest neighbor classifier are adjusted based on feedback. Those features that are the best at discriminating between positive and negative samples receive a more significant weight for the distance computation.
Another technique is to set up an optimization problem as a systematic formulation to the relevance feedback problem. The goal of the optimization problem is to find the optimal linear transformation which maps the feature space into a new space. The new space has the property of clustering together positive examples, and hence makes it easier to separate those positive examples from the negative examples.
Support vector machines may be used for the relevance feedback problem in a content based retrieval system. The support vector machines or SVMs may be incorporated as an automatic tool to evaluate preference weights of the relative images. The weights may then be utilized to compute a query refinement. SVMs can also be directly used to derive similarity matching between different images.
Different techniques have been used in the context of support vector machine methods. The kernel function of such a machine usually has a significant effect on its discrimination ability.